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Related Experiment Videos

Deep Learning Based Framework for Detection and Classification of Leukemia Using Microscopic Images.

Rida Arif1, Shahzad Akbar2, Usama Shahzore3

  • 1Department of Computer Science, Govt. College Women University Faisalabad, Faisalabad, Pakistan.

Microscopy Research and Technique
|June 11, 2026
PubMed
Summary

Related Concept Videos

Classification of Leukocytes01:30

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

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This summary is machine-generated.

This study uses artificial intelligence, specifically deep learning with convolutional neural networks (CNNs), to detect leukemia from microscopic images. The AI model achieved high accuracy, showing promise for computer-aided leukemia diagnosis.

Area of Science:

  • Hematology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Leukemia, a white blood cell cancer, necessitates early diagnosis for effective treatment.
  • Microscopy is vital for leukemia diagnosis, but AI can enhance accuracy and efficiency.
  • Current automated methods still require medical practitioner oversight.

Purpose of the Study:

  • To develop and evaluate a deep learning approach for detecting and classifying leukemia from microscopic images.
  • To leverage convolutional neural networks (CNNs) for enhanced leukemia cell identification.
  • To assess the model's performance in differentiating between leukemia subtypes.

Main Methods:

  • Image pre-processing and data augmentation to expand the microscopic image dataset.
Keywords:
classificationhealth risksleukemiasegmentationwhite blood cells (WBCs)

Related Experiment Videos

  • U-Net model for segmenting leukemia and normal cells for feature extraction.
  • CNN-based architecture for classifying leukemia subtypes using the ASH dataset.
  • 10-fold cross-validation for performance evaluation.

    • Main Results:

    • Achieved 99.06% accuracy in binary classification and 98.68% in multi-class classification.
    • Demonstrated high performance with a recall of 96.74%, precision of 96.83%, and F1-score of 96.77%.
    • The model's performance is comparable to existing leukemia detection methods.

    Conclusions:

    • The proposed framework integrating microscopic imaging and deep learning shows significant potential for computer-aided leukemia diagnosis.
    • Further research with larger, diverse datasets and advanced deep learning models is recommended.
    • The approach could be extended to diagnose other blood disorders.